Understanding social relationships plays an important role in smooth information sharing and project management. Recently, extracting social relationships from activity sensor data has gained popularity, and many researchers have tried to detect close relationship pairs based on the similarities between activity sensor data, namely, unsupervised approaches. However, there is room for further research into social relationship analysis of sensor data in terms of extraction performance. We therefore focus on improving the accuracy of detection and propose a novel fine-grained social relationship extraction from coarse supervision data by supervised approach based on multiple instance learning. In this paper, fine-grained relationship means the relationship including information about the time and duration they are together, and coarse supervision data is the data containing only information about whether they are together in a day. In this research, we evaluate the feasibility of our extraction method and analyze the extracted fine-grained social relationships. Our approach improve detection accuracy and achieve extraction of fine-grained relationships from coarse supervision data.